CNN_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on the GPU\n"
     ]
    }
   ],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device(\"cuda:0\") \n",
    "    print(\"Running on the GPU\")\n",
    "else:\n",
    "    device = torch.device(\"cpu\")\n",
    "    print(\"Running on the CPU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24946\n",
      "(100, 100)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19d47d04fd0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.load('training_data.npy', allow_pickle=True)\n",
    "print(len(data))\n",
    "print(data[0][0].shape)\n",
    "plt.imshow(data[0][0], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, 5)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 5)\n",
    "        self.conv3 = nn.Conv2d(64, 128, 5)\n",
    "        x = torch.randn(100,100).view(-1,1,100,100)\n",
    "        self.flatten_size = None\n",
    "        x = self.conv(x)\n",
    "        self.fc1 = nn.Linear(self.flatten_size, 512)\n",
    "        self.fc2 = nn.Linear(512, 2)\n",
    "    \n",
    "    def conv(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, (2,2))\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, (2,2))\n",
    "        x = F.relu(self.conv3(x))\n",
    "        x = F.max_pool2d(x, (2,2))\n",
    "        if self.flatten_size is None:\n",
    "            self.flatten_size = x[0].shape[0] * x[0].shape[1] * x[0].shape[2]\n",
    "        return x\n",
    "    \n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = x.view(-1, self.flatten_size)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return F.softmax(x, dim=1)\n",
    "    \n",
    "\n",
    "model = Net().to(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "criterion = nn.MSELoss()\n",
    "EPOCHS = 5\n",
    "batch_size = 128  \n",
    "test_percentage = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([22452, 100, 100])\n"
     ]
    }
   ],
   "source": [
    "X = torch.Tensor([x[0] for x in data]).to(device)\n",
    "Y = torch.Tensor([x[1] for x in data]).to(device)\n",
    "X = X/255.0\n",
    "\n",
    "test_size = int(len(X) * test_percentage)\n",
    "X_train = X[:-test_size]\n",
    "Y_train = Y[:-test_size]\n",
    "X_test = X[-test_size:]\n",
    "Y_test = Y[-test_size:]\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_acc(y_pred, y_test):\n",
    "    y_pred_tag = torch.round(torch.sigmoid(y_pred))\n",
    "\n",
    "    correct_results_sum = (y_pred_tag == y_test).sum().float()\n",
    "    acc = correct_results_sum/y_test.shape[0]\n",
    "    acc = acc * 100\n",
    "    \n",
    "    return acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 176/176 [00:59<00:00,  2.94it/s]\n",
      "  0%|                                                                                          | 0/176 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2191, device='cuda:0', grad_fn=<MseLossBackward>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 176/176 [00:58<00:00,  3.03it/s]\n",
      "  0%|                                                                                          | 0/176 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1855, device='cuda:0', grad_fn=<MseLossBackward>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 176/176 [00:58<00:00,  3.03it/s]\n",
      "  0%|                                                                                          | 0/176 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1633, device='cuda:0', grad_fn=<MseLossBackward>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 176/176 [00:58<00:00,  3.03it/s]\n",
      "  0%|                                                                                          | 0/176 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1440, device='cuda:0', grad_fn=<MseLossBackward>)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 176/176 [00:58<00:00,  3.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.1230, device='cuda:0', grad_fn=<MseLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "def train(X, Y, model, EPOCHS, batch_size, criterion, optimizer):\n",
    "    losses = []\n",
    "    accuracies = []\n",
    "    for epoch in range(EPOCHS):\n",
    "        for i in tqdm(range(0, X.shape[0], batch_size)):\n",
    "            x , y = X[i:i+batch_size, :].view(-1,1,X.shape[1],X.shape[2]), Y[i:i+batch_size, :]\n",
    "            model.zero_grad()\n",
    "            optimizer.zero_grad()\n",
    "            y_hat = model.forward(x)\n",
    "            loss = criterion(y_hat,y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            if i%(batch_size*10) == 0:\n",
    "                losses.append(loss)\n",
    "        print(np.min(losses))\n",
    "    return model, accuracies, losses\n",
    "\n",
    "model, accuracies, losses = train(X_train, Y_train, model, EPOCHS, batch_size, criterion, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19d42533668>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████| 22452/22452 [01:09<00:00, 325.31it/s]\n",
      "  3%|██                                                                             | 67/2494 [00:00<00:07, 325.97it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8035809727418493\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 2494/2494 [00:07<00:00, 334.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7666399358460305\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "def accuracy(x, y):\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for i in tqdm(range(len(x))):\n",
    "            real_class = torch.argmax(y[i])\n",
    "            net_out = model(x[i].view(-1, 1, 100, 100))[0]  # returns a list, \n",
    "            predicted_class = torch.argmax(net_out)\n",
    "\n",
    "            if predicted_class == real_class:\n",
    "                correct += 1\n",
    "            total += 1\n",
    "    print(correct/total)\n",
    "accuracy(X_train, Y_train)\n",
    "accuracy(X_test, Y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}